昇腾杀手锏FlashComm，让模型推理单车道变多车道

Core Viewpoint - The article discusses the communication challenges faced by MoE (Mixture of Experts) models in large-scale inference and how Huawei has addressed these issues through innovative solutions to optimize performance. Group 1: Communication Challenges - The rapid growth of MoE model parameters, often exceeding hundreds of billions, poses significant storage and scheduling challenges, leading to increased communication bandwidth demands that can cause network congestion [6][10]. - Traditional communication strategies like AllReduce have limitations, particularly in high concurrency scenarios, where they contribute significantly to end-to-end inference latency [7][11]. - The tensor parallelism (TP) approach, while effective in reducing model weight size, faces challenges with AllReduce operations that exacerbate overall network latency in multi-node deployments [7][12]. Group 2: Huawei's Solutions - Huawei introduced a multi-stream parallel technology that allows for simultaneous processing of different data streams, significantly reducing key path latency and improving performance metrics such as a 10% speedup in the Prefill phase and a 25-30% increase in Decode throughput for the DeepSeek model [12][14]. - The AllReduce operation has been restructured to first sort data intelligently (ReduceScatter) and then broadcast the essential information (AllGather), resulting in a 35% reduction in communication volume and a performance boost of 22-26% in the DeepSeek model's Prefill inference [14][15]. - By adjusting the parallel dimensions of matrix multiplication, Huawei achieved an 86% reduction in communication volume during the attention mechanism transition phase, leading to a 33% overall speedup in inference [15][19]. Group 3: Future Directions - Huawei plans to continue innovating in areas such as multi-stream parallelism, automatic weight prefetching, and model parallelism to further enhance the performance of large-scale MoE model inference systems [19][20].